Mood and emotion are considered distinct phenomena, yet there is a general lack of consensus within the scientific community concerning their definition. Leveraging on a physiology-based definition of these phenomena and their relation, we propose a concept design to define a real-time realworld positive mood supporting application. In particular, the application will be provided through wearable and portable devices, and comprise different blocks related to the user profiling, the identification of proper mood stimuli based on the users' responses, and a mood detection system. Therefore, multiple data will be exploited to provide an effective and efficient positive mood support, starting from user demographics, moving to an ecological momentary assessment of mood, and integrating physiological signal analyses.

This manuscript analyses the performance of different machine learning models classifying hand gestures from electromyography (EMG) signals. The EMG information is obtained from the WyoFlex armband, a wearable bracelet with four sensors that capture the EMG of the forearm. This study considers four different models, the K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Long Short-Term Memories (LSTM) for classifying four and six hand gestures. The methodology comprises different scenarios, including the application of the Synthetic Minority Over-Sampling (SMOTE) algorithm to increase the cardinality of the dataset and the Minimum-Redundancy-Maximum-Relevance (MRMR) technique to reduce computational costs. These models are compared considering the classification of four and six different hand gestures in three different scenarios: a) including the SMOTE technique, b) including the MRMR procedure without SMOTE, and c) including both MRMR and SMOTE. The results show an overall accuracy between 79.32% to 94.90% on the four gestures classification and from 75.14% to 92.80% for six gestures classification, with the SVM classifier producing the best performance in training time and accuracy. The application of the MRMR algorithm yields a reduction of the training time in all models applying the K-fold and Leave-One-Subject-Out (LOSO) cross validation methods, the accuracy is not strongly affected reducing the number of features in almost all the models.

Abstract Cellular Automata have successfully been successfully applied to the modeling and simulation of pedestrian and crowd dynamics. In particular, the investigated scenarios have often been focused on the evaluation of medium–high population density situations, in which the motivation of pedestrians to reach a certain location overcomes their tendency to naturally respect proxemic distances. The global COVID-19 outbreak, though, has shown that sometimes it is crucial to contemplate how proxemic tendencies are emphasized and amplified by the affective state of the individuals involved in the scenario, representing an important factor to take into consideration when investigating the behaviour of a crowd. In this paper we present a research effort aimed at integrating results of quantitative analyses regarding the effects of affective states on the perception of distances maintained by different types of pedestrians with the modeling of pedestrian movement choices in a cellular automata framework.

Warning: This paper contains examples of language and images which may be offensive. Misogyny is a form of hate against women and has been spreading exponentially through the Web, especially on social media platforms. Hateful content towards women can be conveyed not only by text but also using visual and/or audio sources or their combination, highlighting the necessity to address it from a multimodal perspective. One of the predominant forms of multimodal content against women is represented by memes, which are images characterized by pictorial content with an overlaying text introduced a posteriori. Its main aim is originally to be funny and/or ironic, making misogyny recognition in memes even more challenging. In this paper, we investigated 4 unimodal and 3 multimodal approaches to determine which source of information contributes more to the detection of misogynous memes. Moreover, a bias estimation technique is proposed to identify specific elements that compose a meme that could lead to unfair models, together with a bias mitigation strategy based on Bayesian Optimization. The proposed method is able to push the prediction probabilities towards the correct class for up to 61.43% of the cases. Finally, we identified the most challenging archetypes of memes that are still far to be properly recognized, highlighting the most relevant open research directions.

Sensor-based sleep monitoring systems can be used to track sleep behavior on a daily basis and provide feedback to their users to promote health and well-being. Such systems can provide data visualizations to enable self-reflection on sleep habits or a sleep coaching service to improve sleep quality. To provide useful feedback, sleep monitoring systems must be able to recognize whether an individual is sleeping or awake. Existing approaches to infer sleep-wake phases, however, typically assume continuous streams of data to be available at inference time. In real-world settings, though, data streams or data samples may be missing, causing severe performance degradation of models trained on complete data streams. In this paper, we investigate the impact of missing data to recognize sleep and wake, and use regression- and interpolation-based imputation strategies to mitigate the errors that might be caused by incomplete data. To evaluate our approach, we use a data set that includes physiological traces - collected using wristbands -, behavioral data - gathered using smartphones - and self-reports from 16 participants over 30 days. Our results show that the presence of missing sensor data degrades the balanced accuracy of the classifier on average by 10–35 percentage points for detecting sleep and wake depending on the missing data rate. The impu-tation strategies explored in this work increase the performance of the classifier by 4–30 percentage points. These results open up new opportunities to improve the robustness of sleep monitoring systems against missing data.

In this paper, we propose a skin classification method exploiting faces and bodies automatically detected in the image, to adaptively initialize individual ad hoc skin classifiers. Each classifier is initialized by a face and body couple or by a single face, if no reliable body is detected. Thus, the proposed method builds an ad hoc skin classifier for each person in the image, resulting in a classifier less dependent from changes in skin color due to tan levels, races, genders, and illumination conditions. The experimental results on a heterogeneous data set of labeled images show that our proposal outperforms the state-of-the-art methods, and that this improvement is statistically significant.

A new approach for selective edge enhancement using unsharp masking is presented. This is based on the premise that biological vision and image reproduction share common principles. In the traditional approach the high frequency components of the image are emphasized, adding to the signal a constant fraction of its high-pass filtered version. The presence of a linear high-pass filter makes the system extremely sensitive to noise. In our approach, the high frequencies added to input image are weighted by a topographic map corresponding to visually salient regions, obtained by a neurodynamical model of visual attention. In this way, the unsharp masking algorithm becomes local and adaptive, enhancing differently the edges according to human perception.